Cross-cultural studies suggest that access to a conventional language containing words that can be used for counting is essential to develop representations of large exact numbers. However, cultures that lack a conventional counting system typically differ from cultures that have such systems, not only in language but also in many other ways. As a result, it is difficult to isolate the effects of language on the development of number representations. Here we examine the numerical abilities of individuals who lack conventional language for number (deaf individuals who do not have access to a usable model for language, spoken or signed) but who live in a numerate culture (Nicaragua) and thus have access to other aspects of culture that might foster the development of number. These deaf individuals develop their own gestures, called homesigns, to communicate. We show that homesigners use gestures to communicate about number. However, they do not consistently extend the correct number of fingers when communicating about sets greater than three, nor do they always correctly match the number of items in one set to a target set when that target set is greater than three. Thus, even when integrated into a numerate society, individuals who lack input from a conventional language do not spontaneously develop representations of large exact numerosities.
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http://dx.doi.org/10.1073/pnas.1015975108 | DOI Listing |
Sci Rep
December 2024
Department of Earth Sciences, Science Labs, Durham University, Durham, UK.
Claims of industrially induced seismicity vary from indisputable to unpersuasive and yet the veracity of industrial induction is vital for regulatory and operational practice. Assessment schemes have been developed in response to this need. We report here an initial assessment of the reliability of all globally known cases of proposed human-induced earthquakes and invite specialists on particular cases to refine these results.
View Article and Find Full Text PDFJ Imaging
December 2024
College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.
With technological advancements, remarkable progress has been made with the convergence of health sciences and Artificial Intelligence (AI). Modern health systems are proposed to ease patient diagnostics. However, the challenge is to provide AI-based precautions to patients and doctors for more accurate risk assessment.
View Article and Find Full Text PDFJ Pers Med
December 2024
Department of Interdisciplinary Medicine, University of Bari "Aldo Moro", 70124 Bari, Italy.
: The aim of this systematic review was to evaluate the clinical efficacy, benefits, and limitations of piezosurgery in tooth extractions compared to conventional methods. Piezosurgery has emerged as a minimally invasive alternative, promoting better preservation of soft tissues and bone structures. Understanding its impact on postoperative outcomes such as pain, swelling, trismus, and bone healing is critical for its application in oral surgery; We restricted our search to English-language articles published between 1 January 2004 and 28 August 2024, in PubMed, Scopus, and Web of Science.
View Article and Find Full Text PDFJ Pers Med
December 2024
Faculty of General Medicine, University of Medicine, Pharmacy, Sciences and Technology "George Emil Palade" of Târgu Mureş, 540139 Târgu Mureş, Romania.
: Robotic-assisted unicompartmental arthroplasty (rUKA) is gradually gaining more popularity than its conventional counterpart (cUKA). Current studies are highly heterogenic in terms of methodology and the reported results; therefore, establishing the optimal recommendation for patients becomes less straightforward. For this reason, this meta-analysis aims to provide an up-to-date evidence-based analysis on current evidence regarding clinical outcomes and complication rates following rUKA and cUKA.
View Article and Find Full Text PDFOphthalmic Physiol Opt
December 2024
Optometry and Vision Sciences Research Group, Aston University, Birmingham, UK.
Purpose: To propose a novel artificial intelligence (AI)-based virtual assistant trained on tabular clinical data that can provide decision-making support in primary eye care practice and optometry education programmes.
Method: Anonymised clinical data from 1125 complete optometric examinations (2250 eyes; 63% women, 37% men) were used to train different machine learning algorithm models to predict eye examination classification (refractive, binocular vision dysfunction, ocular disorder or any combination of these three options). After modelling, adjustment, mining and preprocessing (one-hot encoding and SMOTE techniques), 75 input (preliminary data, history, oculomotor test and ocular examinations) and three output (refractive, binocular vision status and eye disease) features were defined.
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